Spatial Prediction of Soil Water Content by Bayesian Optimization–Deep Forest Model with Landscape Index and Soil Texture Data DOI Creative Commons

Weihao Yang,

Ruohan Zhen,

Fanxiang Meng

et al.

Agronomy, Journal Year: 2024, Volume and Issue: 14(12), P. 3039 - 3039

Published: Dec. 19, 2024

The accurate prediction of the spatial variability for soil water content (SWC) in farmland is essential resource management and sustainable agricultural development. However, natural factors introduce uncertainty result poor alignment when predicting SWC, leading to low accuracy. To address this, this study introduced a novel indicator: landscape indices. These indices include largest patch index (LPI), edge density (ED), aggregation (AI), cohesion (COH), contagion (CON), division (DIV), percentage like adjacencies (PLA), Shannon evenness (SHEI), diversity (SHDI). A Bayesian optimization–deep forest (BO–DF) model was developed leverage these SWC. Statistical analysis revealed that exhibited skewed distributions weak linear correlations with SWC (r < 0.2). Despite incorporating variables into BO–DF significantly improved accuracy, R2 increasing by 35.85%. This demonstrated robust nonlinear fitting capability Spatial mapping using indicated high-value areas were predominantly located eastern southern regions Yellow River Delta China. Furthermore, SHapley additive explanation (SHAP) highlighted key drivers findings underscore potential as valuable prediction, supporting regional strategies

Language: Английский

Ecological restoration zoning of territorial space in China: An ecosystem health perspective DOI
Wanxu Chen, Tianci Gu,

Jingwei Xiang

et al.

Journal of Environmental Management, Journal Year: 2024, Volume and Issue: 364, P. 121371 - 121371

Published: June 15, 2024

Language: Английский

Citations

24
Linking landscape patterns to rainfall-runoff-sediment relationships: A case study in an agriculture, forest, and urbanization-dominated mountain watershed DOI Creative Commons
Chong Wei, Xiaohua Dong, Yaoming Ma

et al.

Ecological Indicators, Journal Year: 2025, Volume and Issue: 172, P. 113279 - 113279

Published: Feb. 25, 2025

Language: Английский

Citations

0
Characteristics of Ecosystem Services in Megacities Within the Yellow River Basin, Analyzed Through a Resilience Perspective: A Case Study of Xi’an and Jinan DOI Open Access
Bowen Zhang,

Xianglong Tang,

J. J. Cui

et al.

Sustainability, Journal Year: 2025, Volume and Issue: 17(8), P. 3371 - 3371

Published: April 10, 2025

Megacities in developing countries are still undergoing rapid urbanization, with different cities exhibiting ecosystem services (ESs) heterogeneity. Evaluating ESs among various and analyzing the influencing factors from a resilience perspective can effectively enhance ability of to deal react quickly risks uncertainty. This approach is also crucial for optimizing ecological security patterns. study focuses on Xi’an Jinan, two important megacities along Yellow River China. First, we quantified four both cities: carbon storage (CS), habitat quality (HQ), food production (FP), soil conservation (SC). Second, analyzed synergies trade-offs between these using bivariate local spatial autocorrelation Spearman’s rank correlation coefficient. Finally, conducted driver analysis Geographic Detector. Results: (1) The temporal distribution Jinan quite different, but show lower ES levels urban core area. (2) showed strong synergistic effect. Among them, CS-HQ had strongest synergy 0.93. In terms space, north dominated by low–low clustering, while south high–high clustering. FP-SC trade-off effect −0.35 2000, which gradually weakened over time was mainly distributed northern area city where cropland construction were concentrated. (3) Edge density, patch NDVI have greatest influence CS Jinan. DEM, slope, density HQ. Temperature, edge impact temperature FP cities. SC. Landscape fragmentation has great CS, HQ, SC Due insufficient research data, this focused only middle reaches River. However, results provide new solving problem regional sustainable development directions ideas follow-up field.

Language: Английский

Citations

0
Spatial Prediction of Soil Water Content by Bayesian Optimization–Deep Forest Model with Landscape Index and Soil Texture Data DOI Creative Commons

Weihao Yang,

Ruohan Zhen,

Fanxiang Meng

et al.

Agronomy, Journal Year: 2024, Volume and Issue: 14(12), P. 3039 - 3039

Published: Dec. 19, 2024

The accurate prediction of the spatial variability for soil water content (SWC) in farmland is essential resource management and sustainable agricultural development. However, natural factors introduce uncertainty result poor alignment when predicting SWC, leading to low accuracy. To address this, this study introduced a novel indicator: landscape indices. These indices include largest patch index (LPI), edge density (ED), aggregation (AI), cohesion (COH), contagion (CON), division (DIV), percentage like adjacencies (PLA), Shannon evenness (SHEI), diversity (SHDI). A Bayesian optimization–deep forest (BO–DF) model was developed leverage these SWC. Statistical analysis revealed that exhibited skewed distributions weak linear correlations with SWC (r < 0.2). Despite incorporating variables into BO–DF significantly improved accuracy, R2 increasing by 35.85%. This demonstrated robust nonlinear fitting capability Spatial mapping using indicated high-value areas were predominantly located eastern southern regions Yellow River Delta China. Furthermore, SHapley additive explanation (SHAP) highlighted key drivers findings underscore potential as valuable prediction, supporting regional strategies

Language: Английский

Citations

0